Location data provider dataPlor has secured $4 million in funding to expand its operations in Latin America, with longer-term ambitions in Southeast Asia. The company currently operates in Mexico, building business location datasets for brands and technology companies that need accurate location data. Brands listed as dataPlor clients include UberEats, iFood, and American Express. Investors contributing in this round include Space Capital, Quest Venture Partners, and FF Venture Capital.
Michener says dataPlor will use the funds to expand into Argentina, Brazil, Chile, Colombia, and Peru. And the company plans to expand its technology and sales teams.
Finding POIs in Emerging Economies
Los Angeles-based dataPlor is carving out a niche building local business databases in emerging economies, where official data sources are often out of date or inaccurate. And where much of the small business economy is informal. And informal businesses rarely have a digital footprint that can be scraped by a data company. Discovering these businesses requires shoe leather.
Founder and CEO Geoffrey Michener told Localogy Insider in an interview Friday afternoon that the unique challenges involved in collecting location data in emerging economies also makes it a great opportunity. He said that in Mexico, data from official sources is 81% inaccurate, according to dataPlor’s analysis. In Brazil, the figure is more than 50%.
“Global organizations like Uber will not be as successful as they want to be or could be due to this lack of local information,” Geoff said. “And that is what we are solving at dataPlor.”
Michener said dataPlor uses standard data verification methods to confirm the location data from third party sources.
“Every record is human-verified. So whether we are using AI bots, call centers or other mechanisms, we are always calling and making sure information is correct,” he said. “We also pull from third party sources, as anybody would and we will call to verify. Then we record it, digitize it, and update it at scale in our database.”
The Uber of Data Collection
Michener also described how dataPlor uses a network of gig economy workers to collect data on new and informal businesses (convenience stores, cafes, and so on). These gig workers use an app to input information on businesses they could with their own eyes. They upload information like business name, category, lat/long, address, services, hours, payment methods, as well as photos of the location.
“One of the things that happens with human nature is if you can find a shortcut, humans will find a shortcut,” Michener said. “We have implemented a lot of tools.”
Michener said dataPlor learned early on that firm verification methods were needed to make sure the “explorers” weren’t just making things up. The company requires explorers to check in on the app for each location they scout. This establishes their location. And, in addition to checking it, explorers are required to take interior and exterior photos. As well as take photos of any features they report about the business. For example, if they report credit card acceptance, dataPlor needs a photo of the credit card terminal. The main objective is to verify that the explorer actually gathered the information first hand.
“Once we have the information we humanly verify every single business before we pay them,” Michener said. “By doing all of this work, it really disincentives our explores to cheat.”
You can catch the highlights of our discussion with Geoff on building accurate location databases in emerging economies here.